Enterprises are increasingly capturing, storing, and mining a plethora of information related to communications with their customers. Often this information is stored and indexed within databases. Once the information is indexed, queries are developed on an as-needed basis to mine the information from the database for a variety of organizational goals: such as planning, analytics, reporting, etc.
Many times the information stored and indexed is created, mined, updated, and manipulated by application programs created by developers on behalf of analysts.
Often these mining applications desire to aggregate and combine a variety of different data within databases of the enterprise.
Data aggregation is a process in which several datasets are combined and analyzed together, to generate overall results. This process is of particular interest in Demand Chain Management (DCM) applications because of the hierarchical nature of the data. Typical examples are aggregation of data over merchandise and/or location hierarchies.
Performing multi-variable analysis (e.g. multi-regression) at an aggregate level may be required due to various reasons, in particular scarcity of data at low levels of hierarchy.
A variety of issues arise when data is scarce, such as statistical applications can become less reliable. Thus, enterprises attempt to aggregate data from different levels of the hierarchy in an attempt to improve the reliability of enterprise applications by increasing the data points being used with those applications.
But, not just any type of data can be combined. Furthermore, conventional data aggregation approaches are almost exclusively manual processes, because fields of the database from different tables need to be associated and mapped for proper aggregation and heretofore there has been decent automated manner in which to achieve this.
Therefore, it can be seen that improved techniques for aggregating data, which is used as input into database applications, are needed.